Overview

Dataset statistics

Number of variables12
Number of observations5201393
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory515.9 MiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric11

Alerts

DATEUTC has a high cardinality: 52560 distinct valuesHigh cardinality
LC_HUMIDITY is highly overall correlated with LC_RAD and 2 other fieldsHigh correlation
LC_DWPTEMP is highly overall correlated with LC_TEMPHigh correlation
LC_RAD is highly overall correlated with LC_HUMIDITY and 1 other fieldsHigh correlation
LC_RAD60 is highly overall correlated with LC_HUMIDITY and 2 other fieldsHigh correlation
LC_TEMP is highly overall correlated with LC_HUMIDITY and 2 other fieldsHigh correlation
LC_RAININ is highly skewed (γ1 = 47.01423082)Skewed
DATEUTC is uniformly distributedUniform
LC_RAD has 2599146 (50.0%) zerosZeros
LC_RAININ has 5038773 (96.9%) zerosZeros
LC_DAILYRAIN has 4309199 (82.8%) zerosZeros
LC_WINDDIR has 2009326 (38.6%) zerosZeros
LC_WINDSPEED has 2108572 (40.5%) zerosZeros
LC_RAD60 has 2511732 (48.3%) zerosZeros

Reproduction

Analysis started2023-04-24 15:57:20.528655
Analysis finished2023-04-24 15:58:54.757752
Duration1 minute and 34.23 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DATEUTC
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct52560
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size79.4 MiB
2022-09-02 15:40:00
 
108
2022-09-02 09:30:00
 
108
2022-09-01 04:00:00
 
108
2022-09-01 05:10:00
 
108
2022-09-01 05:20:00
 
108
Other values (52555)
5200853 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters98826467
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01 00:10:00
2nd row2022-01-01 00:20:00
3rd row2022-01-01 00:30:00
4th row2022-01-01 00:40:00
5th row2022-01-01 00:50:00

Common Values

ValueCountFrequency (%)
2022-09-02 15:40:00 108
 
< 0.1%
2022-09-02 09:30:00 108
 
< 0.1%
2022-09-01 04:00:00 108
 
< 0.1%
2022-09-01 05:10:00 108
 
< 0.1%
2022-09-01 05:20:00 108
 
< 0.1%
2022-09-01 05:30:00 108
 
< 0.1%
2022-09-01 05:40:00 108
 
< 0.1%
2022-09-01 05:50:00 108
 
< 0.1%
2022-09-01 06:10:00 108
 
< 0.1%
2022-09-01 06:20:00 108
 
< 0.1%
Other values (52550) 5200313
> 99.9%

Length

2023-04-24T17:58:54.794426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:20:00 36310
 
0.3%
17:10:00 36296
 
0.3%
17:00:00 36288
 
0.3%
19:00:00 36288
 
0.3%
18:50:00 36285
 
0.3%
16:50:00 36275
 
0.3%
16:40:00 36271
 
0.3%
17:30:00 36269
 
0.3%
07:20:00 36263
 
0.3%
07:10:00 36261
 
0.3%
Other values (500) 10039980
96.5%

Most occurring characters

ValueCountFrequency (%)
0 30843261
31.2%
2 21008421
21.3%
- 10402786
 
10.5%
: 10402786
 
10.5%
1 8198089
 
8.3%
5201393
 
5.3%
3 2706229
 
2.7%
5 2259823
 
2.3%
4 2215071
 
2.2%
8 1411809
 
1.4%
Other values (3) 4176799
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72819502
73.7%
Dash Punctuation 10402786
 
10.5%
Other Punctuation 10402786
 
10.5%
Space Separator 5201393
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30843261
42.4%
2 21008421
28.8%
1 8198089
 
11.3%
3 2706229
 
3.7%
5 2259823
 
3.1%
4 2215071
 
3.0%
8 1411809
 
1.9%
7 1407574
 
1.9%
6 1384659
 
1.9%
9 1384566
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 10402786
100.0%
Other Punctuation
ValueCountFrequency (%)
: 10402786
100.0%
Space Separator
ValueCountFrequency (%)
5201393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98826467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30843261
31.2%
2 21008421
21.3%
- 10402786
 
10.5%
: 10402786
 
10.5%
1 8198089
 
8.3%
5201393
 
5.3%
3 2706229
 
2.7%
5 2259823
 
2.3%
4 2215071
 
2.2%
8 1411809
 
1.4%
Other values (3) 4176799
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98826467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30843261
31.2%
2 21008421
21.3%
- 10402786
 
10.5%
: 10402786
 
10.5%
1 8198089
 
8.3%
5201393
 
5.3%
3 2706229
 
2.7%
5 2259823
 
2.3%
4 2215071
 
2.2%
8 1411809
 
1.4%
Other values (3) 4176799
 
4.2%

ID
Real number (ℝ)

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.338427
Minimum2
Maximum138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:54.851517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q142
median74
Q3108
95-th percentile130
Maximum138
Range136
Interquartile range (IQR)66

Descriptive statistics

Standard deviation39.500699
Coefficient of variation (CV)0.54605416
Kurtosis-1.1678772
Mean72.338427
Median Absolute Deviation (MAD)34
Skewness-0.13783482
Sum3.7626059 × 108
Variance1560.3052
MonotonicityNot monotonic
2023-04-24T17:58:54.910045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 52544
 
1.0%
123 52542
 
1.0%
22 52540
 
1.0%
111 52539
 
1.0%
124 52537
 
1.0%
19 52535
 
1.0%
112 52533
 
1.0%
117 52519
 
1.0%
80 52519
 
1.0%
105 52500
 
1.0%
Other values (98) 4676085
89.9%
ValueCountFrequency (%)
2 51359
1.0%
3 50285
1.0%
4 49875
1.0%
5 48084
0.9%
6 49191
0.9%
8 37594
0.7%
9 52403
1.0%
10 48086
0.9%
11 42811
0.8%
12 48512
0.9%
ValueCountFrequency (%)
138 22670
0.4%
137 30406
0.6%
136 30142
0.6%
135 26823
0.5%
134 26229
0.5%
133 33364
0.6%
132 33909
0.7%
131 33865
0.7%
130 33866
0.7%
129 33911
0.7%

LC_HUMIDITY
Real number (ℝ)

Distinct88
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.626124
Minimum12
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:54.969943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q164
median82
Q392
95-th percentile99
Maximum99
Range87
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.527435
Coefficient of variation (CV)0.24179006
Kurtosis-0.4223342
Mean76.626124
Median Absolute Deviation (MAD)12
Skewness-0.74815239
Sum3.9856258 × 108
Variance343.26587
MonotonicityNot monotonic
2023-04-24T17:58:55.022391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 330235
 
6.3%
96 159521
 
3.1%
95 157470
 
3.0%
94 155972
 
3.0%
97 152718
 
2.9%
93 149949
 
2.9%
92 147093
 
2.8%
91 140775
 
2.7%
90 140261
 
2.7%
98 137285
 
2.6%
Other values (78) 3530114
67.9%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 40
 
< 0.1%
14 211
 
< 0.1%
15 445
 
< 0.1%
16 648
< 0.1%
17 656
< 0.1%
18 675
< 0.1%
19 706
< 0.1%
20 1070
< 0.1%
21 1480
< 0.1%
ValueCountFrequency (%)
99 330235
6.3%
98 137285
2.6%
97 152718
2.9%
96 159521
3.1%
95 157470
3.0%
94 155972
3.0%
93 149949
2.9%
92 147093
2.8%
91 140775
2.7%
90 140261
2.7%

LC_DWPTEMP
Real number (ℝ)

Distinct3505
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1473083
Minimum-12.6
Maximum25.78
Zeros4415
Zeros (%)0.1%
Negative430919
Negative (%)8.3%
Memory size79.4 MiB
2023-04-24T17:58:55.081399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-12.6
5-th percentile-1.45
Q14.25
median8.76
Q312.28
95-th percentile16.27
Maximum25.78
Range38.38
Interquartile range (IQR)8.03

Descriptive statistics

Standard deviation5.4547058
Coefficient of variation (CV)0.66951017
Kurtosis-0.40094307
Mean8.1473083
Median Absolute Deviation (MAD)3.96
Skewness-0.37695549
Sum42377352
Variance29.753815
MonotonicityNot monotonic
2023-04-24T17:58:55.138246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.39 9706
 
0.2%
8.89 9562
 
0.2%
9.28 9312
 
0.2%
9.22 9196
 
0.2%
9.11 9183
 
0.2%
9.5 9112
 
0.2%
9.61 9060
 
0.2%
6.61 8616
 
0.2%
9.72 8572
 
0.2%
9.78 8570
 
0.2%
Other values (3495) 5110504
98.3%
ValueCountFrequency (%)
-12.6 3
< 0.1%
-12.5 2
< 0.1%
-12.4 1
 
< 0.1%
-12.34 1
 
< 0.1%
-12.33 2
< 0.1%
-12.3 2
< 0.1%
-12.29 1
 
< 0.1%
-12.28 2
< 0.1%
-12.25 2
< 0.1%
-12.24 1
 
< 0.1%
ValueCountFrequency (%)
25.78 1
< 0.1%
24.27 1
< 0.1%
24.22 1
< 0.1%
24.21 1
< 0.1%
24.17 1
< 0.1%
24.15 1
< 0.1%
23.89 1
< 0.1%
23.83 1
< 0.1%
23.8 1
< 0.1%
23.78 1
< 0.1%

LC_n
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.386937
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:55.197489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q137
median37
Q338
95-th percentile38
Maximum47
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.6837961
Coefficient of variation (CV)0.1888775
Kurtosis12.8184
Mean35.386937
Median Absolute Deviation (MAD)1
Skewness-3.6727055
Sum1.8406137 × 108
Variance44.673131
MonotonicityNot monotonic
2023-04-24T17:58:55.249156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
37 2136855
41.1%
38 2121990
40.8%
35 168433
 
3.2%
32 152223
 
2.9%
36 152160
 
2.9%
31 73838
 
1.4%
10 19816
 
0.4%
30 18849
 
0.4%
3 18618
 
0.4%
8 18373
 
0.4%
Other values (37) 320238
 
6.2%
ValueCountFrequency (%)
1 11236
0.2%
2 18098
0.3%
3 18618
0.4%
4 17262
0.3%
5 17194
0.3%
6 16255
0.3%
7 16289
0.3%
8 18373
0.4%
9 16256
0.3%
10 19816
0.4%
ValueCountFrequency (%)
47 4
 
< 0.1%
46 1
 
< 0.1%
45 7
 
< 0.1%
44 11
 
< 0.1%
43 15
 
< 0.1%
42 22
 
< 0.1%
41 28
 
< 0.1%
40 38
 
< 0.1%
39 3391
 
0.1%
38 2121990
40.8%

LC_RAD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct915
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.441832
Minimum0
Maximum994
Zeros2599146
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:55.304366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q385
95-th percentile446
Maximum994
Range994
Interquartile range (IQR)85

Descriptive statistics

Standard deviation144.18438
Coefficient of variation (CV)1.814968
Kurtosis4.1875575
Mean79.441832
Median Absolute Deviation (MAD)1
Skewness2.2014847
Sum4.1320819 × 108
Variance20789.136
MonotonicityNot monotonic
2023-04-24T17:58:55.359386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2599146
50.0%
1 44587
 
0.9%
4 37275
 
0.7%
3 33305
 
0.6%
2 31764
 
0.6%
5 27346
 
0.5%
6 21887
 
0.4%
7 19965
 
0.4%
23 19301
 
0.4%
22 19278
 
0.4%
Other values (905) 2347539
45.1%
ValueCountFrequency (%)
0 2599146
50.0%
1 44587
 
0.9%
2 31764
 
0.6%
3 33305
 
0.6%
4 37275
 
0.7%
5 27346
 
0.5%
6 21887
 
0.4%
7 19965
 
0.4%
8 18610
 
0.4%
9 17127
 
0.3%
ValueCountFrequency (%)
994 1
< 0.1%
975 1
< 0.1%
950 1
< 0.1%
949 1
< 0.1%
948 2
< 0.1%
946 1
< 0.1%
932 2
< 0.1%
929 2
< 0.1%
927 1
< 0.1%
926 1
< 0.1%

LC_RAININ
Real number (ℝ)

SKEWED  ZEROS 

Distinct109
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00010329541
Minimum0
Maximum0.38
Zeros5038773
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:55.415774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.38
Range0.38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00099938377
Coefficient of variation (CV)9.6750069
Kurtosis8268.0203
Mean0.00010329541
Median Absolute Deviation (MAD)0
Skewness47.014231
Sum537.28
Variance9.9876792 × 10-7
MonotonicityNot monotonic
2023-04-24T17:58:55.470616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5038773
96.9%
0.002 52394
 
1.0%
0.001 45502
 
0.9%
0.003 26050
 
0.5%
0.004 11383
 
0.2%
0.005 6730
 
0.1%
0.006 5039
 
0.1%
0.007 3241
 
0.1%
0.008 2394
 
< 0.1%
0.009 1670
 
< 0.1%
Other values (99) 8217
 
0.2%
ValueCountFrequency (%)
0 5038773
96.9%
0.001 45502
 
0.9%
0.002 52394
 
1.0%
0.003 26050
 
0.5%
0.004 11383
 
0.2%
0.005 6730
 
0.1%
0.006 5039
 
0.1%
0.007 3241
 
0.1%
0.008 2394
 
< 0.1%
0.009 1670
 
< 0.1%
ValueCountFrequency (%)
0.38 1
< 0.1%
0.335 1
< 0.1%
0.167 1
< 0.1%
0.141 2
< 0.1%
0.129 1
< 0.1%
0.123 1
< 0.1%
0.114 1
< 0.1%
0.111 1
< 0.1%
0.11 1
< 0.1%
0.109 1
< 0.1%

LC_DAILYRAIN
Real number (ℝ)

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001219403
Minimum0
Maximum0.154
Zeros4309199
Zeros (%)82.8%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:55.528883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.007
Maximum0.154
Range0.154
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0051042783
Coefficient of variation (CV)4.1858832
Kurtosis337.45867
Mean0.001219403
Median Absolute Deviation (MAD)0
Skewness14.211986
Sum6342.594
Variance2.6053657 × 10-5
MonotonicityNot monotonic
2023-04-24T17:58:55.583627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4309199
82.8%
0.002 180517
 
3.5%
0.001 117857
 
2.3%
0.003 81411
 
1.6%
0.004 80393
 
1.5%
0.007 72072
 
1.4%
0.005 60223
 
1.2%
0.006 53801
 
1.0%
0.008 36683
 
0.7%
0.009 28413
 
0.5%
Other values (133) 180824
 
3.5%
ValueCountFrequency (%)
0 4309199
82.8%
0.001 117857
 
2.3%
0.002 180517
 
3.5%
0.003 81411
 
1.6%
0.004 80393
 
1.5%
0.005 60223
 
1.2%
0.006 53801
 
1.0%
0.007 72072
 
1.4%
0.008 36683
 
0.7%
0.009 28413
 
0.5%
ValueCountFrequency (%)
0.154 5
 
< 0.1%
0.153 14
 
< 0.1%
0.152 92
 
< 0.1%
0.151 786
< 0.1%
0.15 114
 
< 0.1%
0.149 15
 
< 0.1%
0.148 10
 
< 0.1%
0.147 5
 
< 0.1%
0.146 13
 
< 0.1%
0.145 9
 
< 0.1%

LC_WINDDIR
Real number (ℝ)

Distinct360
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.7668467
Minimum-179
Maximum180
Zeros2009326
Zeros (%)38.6%
Negative1706282
Negative (%)32.8%
Memory size79.4 MiB
2023-04-24T17:58:55.640940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-179
5-th percentile-158
Q1-56
median0
Q328
95-th percentile154
Maximum180
Range359
Interquartile range (IQR)84

Descriptive statistics

Standard deviation87.305095
Coefficient of variation (CV)-15.139139
Kurtosis-0.34495828
Mean-5.7668467
Median Absolute Deviation (MAD)41
Skewness0.026819332
Sum-29995636
Variance7622.1797
MonotonicityNot monotonic
2023-04-24T17:58:55.692036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2009326
38.6%
-146 13718
 
0.3%
-144 13696
 
0.3%
-147 13694
 
0.3%
-145 13617
 
0.3%
-143 13548
 
0.3%
-148 13527
 
0.3%
-149 13376
 
0.3%
-142 13331
 
0.3%
-140 13291
 
0.3%
Other values (350) 3070269
59.0%
ValueCountFrequency (%)
-179 10505
0.2%
-178 10710
0.2%
-177 10996
0.2%
-176 10885
0.2%
-175 11254
0.2%
-174 11275
0.2%
-173 11566
0.2%
-172 11784
0.2%
-171 11913
0.2%
-170 12017
0.2%
ValueCountFrequency (%)
180 10536
0.2%
179 10309
0.2%
178 10151
0.2%
177 10039
0.2%
176 9800
0.2%
175 9720
0.2%
174 9610
0.2%
173 9679
0.2%
172 9692
0.2%
171 9596
0.2%

LC_WINDSPEED
Real number (ℝ)

Distinct891
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28588148
Minimum0
Maximum13.7
Zeros2108572
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:55.747037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.04
Q30.31
95-th percentile1.4
Maximum13.7
Range13.7
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.56425252
Coefficient of variation (CV)1.9737288
Kurtosis20.121346
Mean0.28588148
Median Absolute Deviation (MAD)0.04
Skewness3.7003953
Sum1486982
Variance0.3183809
MonotonicityNot monotonic
2023-04-24T17:58:55.800325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2108572
40.5%
0.01 184266
 
3.5%
0.02 152146
 
2.9%
0.03 121551
 
2.3%
0.04 104820
 
2.0%
0.05 92702
 
1.8%
0.06 83655
 
1.6%
0.07 76216
 
1.5%
0.08 69690
 
1.3%
0.09 64808
 
1.2%
Other values (881) 2142967
41.2%
ValueCountFrequency (%)
0 2108572
40.5%
0.01 184266
 
3.5%
0.02 152146
 
2.9%
0.03 121551
 
2.3%
0.04 104820
 
2.0%
0.05 92702
 
1.8%
0.06 83655
 
1.6%
0.07 76216
 
1.5%
0.08 69690
 
1.3%
0.09 64808
 
1.2%
ValueCountFrequency (%)
13.7 1
< 0.1%
12.2 1
< 0.1%
12.15 1
< 0.1%
11.44 1
< 0.1%
11.21 1
< 0.1%
10.71 1
< 0.1%
10.62 1
< 0.1%
10.45 1
< 0.1%
10.42 1
< 0.1%
10.41 1
< 0.1%

LC_RAD60
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct861
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.430396
Minimum0
Maximum894
Zeros2511732
Zeros (%)48.3%
Negative0
Negative (%)0.0%
Memory size79.4 MiB
2023-04-24T17:58:55.864357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q388
95-th percentile434
Maximum894
Range894
Interquartile range (IQR)88

Descriptive statistics

Standard deviation141.37496
Coefficient of variation (CV)1.7798598
Kurtosis3.8537509
Mean79.430396
Median Absolute Deviation (MAD)2
Skewness2.1309413
Sum4.131487 × 108
Variance19986.881
MonotonicityNot monotonic
2023-04-24T17:58:55.926811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2511732
48.3%
1 81754
 
1.6%
2 49010
 
0.9%
3 41212
 
0.8%
4 39387
 
0.8%
5 26419
 
0.5%
6 22233
 
0.4%
7 20495
 
0.4%
8 18710
 
0.4%
25 18255
 
0.4%
Other values (851) 2372186
45.6%
ValueCountFrequency (%)
0 2511732
48.3%
1 81754
 
1.6%
2 49010
 
0.9%
3 41212
 
0.8%
4 39387
 
0.8%
5 26419
 
0.5%
6 22233
 
0.4%
7 20495
 
0.4%
8 18710
 
0.4%
9 18201
 
0.3%
ValueCountFrequency (%)
894 1
< 0.1%
878 1
< 0.1%
873 1
< 0.1%
870 1
< 0.1%
869 1
< 0.1%
865 1
< 0.1%
860 1
< 0.1%
859 1
< 0.1%
856 1
< 0.1%
855 1
< 0.1%

LC_TEMP
Real number (ℝ)

Distinct4567782
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.606175
Minimum-11.4
Maximum42.161164
Zeros0
Zeros (%)0.0%
Negative177244
Negative (%)3.4%
Memory size79.4 MiB
2023-04-24T17:58:55.994497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.4
5-th percentile1.0325705
Q17.387517
median12.349102
Q317.66019
95-th percentile25.004656
Maximum42.161164
Range53.561164
Interquartile range (IQR)10.272673

Descriptive statistics

Standard deviation7.3911548
Coefficient of variation (CV)0.58631224
Kurtosis-0.18548579
Mean12.606175
Median Absolute Deviation (MAD)5.1488284
Skewness0.1501703
Sum65569671
Variance54.62917
MonotonicityNot monotonic
2023-04-24T17:58:56.050064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 1400
 
< 0.1%
10.2 1360
 
< 0.1%
11.9 1335
 
< 0.1%
10.3 1334
 
< 0.1%
10.9 1333
 
< 0.1%
13.3 1333
 
< 0.1%
13.4 1310
 
< 0.1%
11.3 1288
 
< 0.1%
13.6 1282
 
< 0.1%
10.1 1281
 
< 0.1%
Other values (4567772) 5188137
99.7%
ValueCountFrequency (%)
-11.4 3
< 0.1%
-11.3 2
< 0.1%
-11.2 1
 
< 0.1%
-11.18447 1
 
< 0.1%
-11.17442 2
< 0.1%
-11.12442 1
 
< 0.1%
-11.1 1
 
< 0.1%
-11.09022 2
< 0.1%
-11.07447 1
 
< 0.1%
-11.05952 1
 
< 0.1%
ValueCountFrequency (%)
42.16116354 1
< 0.1%
42.12416033 1
< 0.1%
42.02266235 1
< 0.1%
41.95165925 1
< 0.1%
41.93676939 1
< 0.1%
41.85961032 1
< 0.1%
41.83010052 1
< 0.1%
41.70123848 1
< 0.1%
41.36824861 1
< 0.1%
41.23725765 1
< 0.1%

Interactions

2023-04-24T17:58:40.701962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:05.546659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:09.105277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:12.564203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:16.023570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:19.689866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:23.096920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:26.442088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:29.906073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:33.511654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:37.030240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:41.012599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:05.950938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:09.409622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:12.879642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:16.349933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:20.009692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:23.398309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:26.752719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:30.233238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:33.826312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:37.353397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:41.327166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:06.257895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:09.718359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:13.167946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:16.663166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:20.321232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:23.711451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:27.066396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:30.570589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:34.147339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:37.682173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:41.637978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:06.570775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:10.052514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:13.480567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:17.096205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:20.647403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:24.016256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:27.391222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:30.913325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:34.471013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:38.033786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:41.941402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:06.877177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:10.367855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:13.788418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:17.435856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:20.940143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:24.313364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:27.695940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:31.235935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:34.779989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:38.345708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:42.255856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:07.243004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:10.679801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:14.110203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:17.754404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:21.249247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:24.592869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:28.009591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:31.565693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:35.099463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:38.662877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:42.567981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:07.546846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:10.994309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:14.423560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:18.073633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:21.559749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:24.904103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:28.298816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:31.898785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:35.420041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:38.985557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:42.886158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:07.858290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:11.309799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:14.742375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:18.390629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:21.874840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:25.213404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:28.619435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:32.213658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:35.745332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:39.305332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:43.203043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:08.167387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:11.625617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:15.061824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:18.713057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:22.184244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:25.517809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:28.924747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:32.550197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:36.039649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:39.623411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:43.513580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:08.471829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:11.943673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:15.378855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:19.034940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:22.496086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:25.825711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:29.232065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:32.877343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:36.368202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:39.920572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:43.800078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:08.768569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:12.257516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:15.692449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:19.349729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:22.800835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:26.137833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:29.572580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:33.202107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:36.694213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-24T17:58:40.389212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-24T17:58:56.315449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
IDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDLC_RAD60LC_TEMP
ID1.000-0.0640.0090.0530.001-0.0010.0020.0380.0500.0020.038
LC_HUMIDITY-0.0641.000-0.1690.016-0.5520.1780.261-0.048-0.293-0.607-0.625
LC_DWPTEMP0.009-0.1691.000-0.0050.2250.0430.079-0.027-0.0070.2410.840
LC_n0.0530.016-0.0051.000-0.0100.001-0.005-0.004-0.017-0.010-0.008
LC_RAD0.001-0.5520.225-0.0101.000-0.053-0.051-0.0010.3500.9540.482
LC_RAININ-0.0010.1780.0430.001-0.0531.0000.359-0.0200.061-0.048-0.046
LC_DAILYRAIN0.0020.2610.079-0.005-0.0510.3591.000-0.0660.136-0.035-0.068
LC_WINDDIR0.038-0.048-0.027-0.004-0.001-0.020-0.0661.000-0.093-0.0020.012
LC_WINDSPEED0.050-0.293-0.007-0.0170.3500.0610.136-0.0931.0000.3550.136
LC_RAD600.002-0.6070.241-0.0100.954-0.048-0.035-0.0020.3551.0000.522
LC_TEMP0.038-0.6250.840-0.0080.482-0.046-0.0680.0120.1360.5221.000

Missing values

2023-04-24T17:58:44.277525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-24T17:58:46.888021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DATEUTCIDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDLC_RAD60LC_TEMP
02022-01-01 00:10:0029211.783800.00.0-169.00.430.013.048027
12022-01-01 00:20:0029211.733700.00.0-170.00.330.012.985849
22022-01-01 00:30:0029211.733800.00.0-167.00.460.012.950322
32022-01-01 00:40:0029211.723700.00.0-160.00.520.012.949550
42022-01-01 00:50:0029211.723800.00.0-166.00.510.012.952268
52022-01-01 01:00:0029211.723700.00.0-158.00.930.012.938731
62022-01-01 01:10:0029211.713800.00.0-161.00.540.012.949960
72022-01-01 01:20:0029111.623700.00.0-163.00.710.012.960576
82022-01-01 01:30:0029111.613800.00.0-160.00.540.012.980432
92022-01-01 01:40:0029111.613700.00.0-163.00.850.012.963181
DATEUTCIDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDLC_RAD60LC_TEMP
55468702022-12-31 22:30:00138536.493200.00.002-70.01.450.016.39359
55468712022-12-31 22:40:00138526.323100.00.002-52.01.230.016.48690
55468722022-12-31 22:50:00138516.303200.00.002-64.01.040.016.63222
55468732022-12-31 23:00:00138506.213100.00.002-61.01.430.016.71759
55468742022-12-31 23:10:00138506.163200.00.002-62.01.370.016.77703
55468752022-12-31 23:20:00138506.053200.00.000-49.00.670.016.76285
55468762022-12-31 23:30:00138506.003200.00.000-65.01.390.016.70722
55468772022-12-31 23:40:00138505.903100.00.000-41.01.250.016.60001
55468782022-12-31 23:50:00138505.893200.00.000-51.01.220.016.50053
55468792023-01-01 00:00:00138505.783100.00.000-53.01.080.016.37461